Non-invasive quantification of tomato (Solanum lycopersicum L.) plant biomass through digital imaging using phenomics platform

Laxman, R. H.; Hemamalini, P.; Bhatt, R. M.; Sadashiva, A. T.

doi:10.1007/s40502-018-0374-8

Cited by 21 publications

(16 citation statements)

References 13 publications

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“…Figure 2 presents plots of these three responses. Using these, time intervals with end points at DAPs 18,22,27,33,39,43 and 51 were chosen. For the analysis, the sPSA values for each of these end points were extracted and the mean sPSA AGR and sPSA RGR calculated for each of the intervals DAP 18-22, 22-27, 27-33, 33-39, 39-43, 43-51.…”

Section: A Set-based Analysis Of the Tomato Datamentioning

confidence: 99%

“…They show that only Zn had a significant effect (p ≤ 0.05) on sPSA at DAPs 18 and 22; subsequent to these DAPs (viz. DAPs 27,33,39,43 To illustrate these effects, predicted values for each of the responses sPSA, sPSA AGR and sPSA RGR are combined and each presented in its own plot in Fig. 3.…”

Section: A Set-based Analysis Of the Tomato Datamentioning

confidence: 99%

“…From these images the PSA of the plant was obtained by summing the areas as measured (in kilopixels or kpixels) from two side views at an angular separation of 90° and a view from above [2]. This resulted in 1120 PSA values, which are used as a measure of plant biomass, having been shown to be related to plant fresh weight for numerous species [2,12,[32][33][34]. Note that watering was unintentionally interrupted for 3 days (39-41 DAP inclusive).…”

Section: Plant Growth and Data Acquisitionmentioning

confidence: 99%

“…1. sPSA, sPSA AGR and sPSA RGR at DAPs 18,22,27,33,39,43,51;2. the sPSA AGR and sPSA RGR for each of the intervals DAP 18-22, 22-27, 27-33, 33-39, 39-43, 43-51.…”

Section: Extracting Per-cart Growth Traitsmentioning

confidence: 99%

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Smoothing and extraction of traits in the growth analysis of noninvasive phenotypic data

et al. 2020

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Background: Non-destructive high-throughput plant phenotyping is becoming increasingly used and various methods for growth analysis have been proposed. Traditional longitudinal or repeated measures analyses that model growth using statistical models are common. However, often the variation in the data is inappropriately modelled, in part because the required models are complicated and difficult to fit. We provide a novel, computationally efficient technique that is based on smoothing and extraction of traits (SET), which we compare with the alternative traditional longitudinal analysis methods. Results:The SET-based and longitudinal analyses were applied to a tomato experiment to investigate the effects on plant growth of zinc (Zn) addition and growing plants in soil inoculated with arbuscular mycorrhizal fungi (AMF). Conclusions from the SET-based and longitudinal analyses are similar, although the former analysis results in more significant differences. They showed that added Zn had little effect on plants grown in inoculated soils, but that growth depended on the amount of added Zn for plants grown in uninoculated soils. The longitudinal analysis of the unsmoothed data fitted a mixed model that involved both fixed and random regression modelling with splines, as well as allowing for unequal variances and autocorrelation between time points. Conclusions:A SET-based analysis can be used in any situation in which a traditional longitudinal analysis might be applied, especially when there are many observed time points. Two reasons for deploying the SET-based method are (i) biologically relevant growth parameters are required that parsimoniously describe growth, usually focussing on a small number of intervals, and/or (ii) a computationally efficient method is required for which a valid analysis is easier to achieve, while still capturing the essential features of the exhibited growth dynamics. Also discussed are the statistical models that need to be considered for traditional longitudinal analyses and it is demonstrated that the oft-omitted unequal variances and autocorrelation may be required for a valid longitudinal analysis. With respect to the separate issue of the subjective choice of mathematical growth functions or splines to characterize growth, it is recommended that, for both SET-based and longitudinal analyses, an evidence-based procedure is adopted.

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Section: A Set-based Analysis Of the Tomato Datamentioning

confidence: 99%

Section: A Set-based Analysis Of the Tomato Datamentioning

confidence: 99%

Section: Plant Growth and Data Acquisitionmentioning

confidence: 99%

“…1. sPSA, sPSA AGR and sPSA RGR at DAPs 18,22,27,33,39,43,51;2. the sPSA AGR and sPSA RGR for each of the intervals DAP 18-22, 22-27, 27-33, 33-39, 39-43, 43-51.…”

Section: Extracting Per-cart Growth Traitsmentioning

confidence: 99%

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Smoothing and extraction of traits in the growth analysis of noninvasive phenotypic data

et al. 2020

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“…The use of advanced imaging systems, sensors, automations, and computational resources for the phenotyping in plants make phenomics a high-throughput approach capable of handling thousands of genotypes for the evaluation of hundreds of phenotypic parameters simultaneously [100][101][102]. There are a number of phenomics platforms available such as scan analyzer 3D, used to investigate physiological parameters in tomato plants under drought conditions [103].…”

Section: Phenomic Advances For Abiotic Stress Tolerance In Tomatomentioning

confidence: 99%

Advances in Omics Approaches for Abiotic Stress Tolerance in Tomato

Chaudhary

Khatri

Singla

et al. 2019

Biology

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Tomato, one of the most important crops worldwide, has a high demand in the fresh fruit market and processed food industries. Despite having considerably high productivity, continuous supply as per the market demand is hard to achieve, mostly because of periodic losses occurring due to biotic as well as abiotic stresses. Although tomato is a temperate crop, it is grown in almost all the climatic zones because of widespread demand, which makes it challenge to adapt in diverse conditions. Development of tomato cultivars with enhanced abiotic stress tolerance is one of the most sustainable approaches for its successful production. In this regard, efforts are being made to understand the stress tolerance mechanism, gene discovery, and interaction of genetic and environmental factors. Several omics approaches, tools, and resources have already been developed for tomato growing. Modern sequencing technologies have greatly accelerated genomics and transcriptomics studies in tomato. These advancements facilitate Quantitative trait loci (QTL) mapping, genome-wide association studies (GWAS), and genomic selection (GS). However, limited efforts have been made in other omics branches like proteomics, metabolomics, and ionomics. Extensive cataloging of omics resources made here has highlighted the need for integration of omics approaches for efficient utilization of resources and a better understanding of the molecular mechanism. The information provided here will be helpful to understand the plant responses and the genetic regulatory networks involved in abiotic stress tolerance and efficient utilization of omics resources for tomato crop improvement.

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